Structure Learning for Epidemic Processes on Graphs
Event details
| Date | 13.07.2023 |
| Hour | 13:45 › 15:00 |
| Speaker | Paula Mürmann |
| Location | |
| Category | Conferences - Seminars |
EDIC candidacy exam
Exam president: Prof. Pascal Frossard
Thesis advisor: Prof. Patrick Thiran
Co-examiner: Prof. Yanina Shkel
Abstract
Abstract—Structural inference is an essential task in modelling
any kind of correlation data as a graphical network. We discuss
the structure learning problem based on data obtained from
epidemic spreading processes on graphs, where the infection state
of a node depends on the state of its neighbours. We will link
this inference problem to epidemic prediction problems such as
the influence maximisation problem and discuss ideas to tailor
the learning approach to the prediction task.
Background papers
David Kempe, Jon Kleinberg, and Éva Tardos (Aug. 24, 2003). “Maximizing the spread
of influence through a social network”
https://dl.acm.org/doi/10.1145/956750.956769
Manuel Gomez-Rodriguez, Jure Leskovec, and Andreas Krause (Feb. 1, 2012). “Inferring
Networks of Diffusion and Influence”
https://dl.acm.org/doi/10.1145/2086737.2086741
Mateusz Wilinski and Andrey Lokhov (July 1, 2021). “Prediction-Centric Learning of
Independent Cascade Dynamics from Partial Observations
https://proceedings.mlr.press/v139/wilinski21a.html
Exam president: Prof. Pascal Frossard
Thesis advisor: Prof. Patrick Thiran
Co-examiner: Prof. Yanina Shkel
Abstract
Abstract—Structural inference is an essential task in modelling
any kind of correlation data as a graphical network. We discuss
the structure learning problem based on data obtained from
epidemic spreading processes on graphs, where the infection state
of a node depends on the state of its neighbours. We will link
this inference problem to epidemic prediction problems such as
the influence maximisation problem and discuss ideas to tailor
the learning approach to the prediction task.
Background papers
David Kempe, Jon Kleinberg, and Éva Tardos (Aug. 24, 2003). “Maximizing the spread
of influence through a social network”
https://dl.acm.org/doi/10.1145/956750.956769
Manuel Gomez-Rodriguez, Jure Leskovec, and Andreas Krause (Feb. 1, 2012). “Inferring
Networks of Diffusion and Influence”
https://dl.acm.org/doi/10.1145/2086737.2086741
Mateusz Wilinski and Andrey Lokhov (July 1, 2021). “Prediction-Centric Learning of
Independent Cascade Dynamics from Partial Observations
https://proceedings.mlr.press/v139/wilinski21a.html
Practical information
- General public
- Free
Contact
- edic@epfl.ch